Department of Neurology, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, Yunnan, China.
Department of Emergency, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, Yunnan, China.
Sci Rep. 2024 Feb 15;14(1):3803. doi: 10.1038/s41598-024-54555-2.
Studies have shown that a series of molecular events caused by oxidative stress is associated with ferroptosis and oxidation after ischemic stroke (IS). Differential analysis was performed to identify differentially expressed mRNA (DEmRNAs) between IS and control groups. Critical module genes were identified using weighted gene co-expression network analysis (WGCNA). DEmRNAs, critical module genes, oxidative stress-related genes (ORGs), and ferroptosis-related genes (FRGs) were crossed to screen for intersection mRNAs. Candidate mRNAs were screened based on the protein-protein interaction (PPI) network and the MCODE plug-in. Biomarkers were identified based on two types of machine learning algorithms, and the intersection was obtained. Functional items and related pathways of the biomarkers were identified using gene set enrichment analysis (GSEA). Finally, single-sample GSEA (ssGSEA) and Wilcoxon tests were used to identify differential immune cells. An miRNA-mRNA-TF network was created. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to verify the expression levels of biomarkers in the IS and control groups. There were 8287 DE mRNAs between the IS and control groups. The genes in the turquoise module were selected as critical module genes for IS. Thirty intersecting mRNAs were screened for overlaps. Seventeen candidate mRNAs were also identified. Four biomarkers (CDKN1A, GPX4, PRDX1, and PRDX6) were identified using two types of machine-learning algorithms. GSEA results indicated that the biomarkers were associated with steroid biosynthesis. Nine types of immune cells (activated B cells and neutrophils) were markedly different between the IS and control groups. We identified 3747 miRNA-mRNA-TF regulatory pairs in the miRNA-mRNA-TF regulatory network, including hsa-miR-4469-CDKN1A-BACH2 and hsa-miR-188-3p-GPX4-ATF2. CDKN1A, PRDX1, and PRDX6 were upregulated in IS samples compared with control samples. This study suggests that four biomarkers (CDKN1A, GPX4, PRDX1, and PRDX6) are significantly associated with IS. This study provides a new reference for the diagnosis and treatment of IS.
研究表明，氧化应激引起的一系列分子事件与缺血性中风（IS）后的铁死亡和氧化有关。通过差异分析来识别 IS 组和对照组之间差异表达的 mRNA（DEmRNAs）。使用加权基因共表达网络分析（WGCNA）鉴定关键模块基因。将 DEmRNAs、关键模块基因、氧化应激相关基因（ORGs）和铁死亡相关基因（FRGs）交叉筛选以获得交集 mRNA。基于蛋白质-蛋白质相互作用（PPI）网络和 MCODE 插件筛选候选 mRNA。基于两种类型的机器学习算法识别生物标志物，并获得交集。基于基因集富集分析（GSEA）识别生物标志物的功能项目和相关途径。最后，使用单样本 GSEA（ssGSEA）和 Wilcoxon 检验鉴定差异免疫细胞。创建 miRNA-mRNA-TF 网络。通过定量实时聚合酶链反应（qRT-PCR）验证 IS 组和对照组中生物标志物的表达水平。IS 组和对照组之间有 8287 个差异表达的 mRNAs。选择绿松石模块中的基因作为 IS 的关键模块基因。筛选 30 个交集 mRNA 进行重叠。还鉴定了 17 个候选 mRNA。使用两种类型的机器学习算法鉴定了 4 个生物标志物（CDKN1A、GPX4、PRDX1 和 PRDX6）。GSEA 结果表明，生物标志物与类固醇生物合成有关。IS 组和对照组之间有 9 种免疫细胞（活化 B 细胞和中性粒细胞）明显不同。在 miRNA-mRNA-TF 调控网络中鉴定了 3747 个 miRNA-mRNA-TF 调控对，包括 hsa-miR-4469-CDKN1A-BACH2 和 hsa-miR-188-3p-GPX4-ATF2。与对照样本相比，IS 样本中 CDKN1A、PRDX1 和 PRDX6 上调。该研究表明，四个生物标志物（CDKN1A、GPX4、PRDX1 和 PRDX6）与 IS 显著相关。该研究为 IS 的诊断和治疗提供了新的参考。
